Overview

Dataset statistics

Number of variables13
Number of observations898
Missing cells456
Missing cells (%)3.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory219.4 KiB
Average record size in memory250.2 B

Variable types

Numeric10
Categorical3

Alerts

nombre has a high cardinality: 898 distinct values High cardinality
id is highly correlated with generacionHigh correlation
generacion is highly correlated with idHigh correlation
peso is highly correlated with altura and 3 other fieldsHigh correlation
altura is highly correlated with peso and 2 other fieldsHigh correlation
hp is highly correlated with peso and 2 other fieldsHigh correlation
attack is highly correlated with peso and 3 other fieldsHigh correlation
defense is highly correlated with peso and 2 other fieldsHigh correlation
s_attack is highly correlated with s_defenseHigh correlation
s_defense is highly correlated with defense and 1 other fieldsHigh correlation
id is highly correlated with generacionHigh correlation
generacion is highly correlated with idHigh correlation
peso is highly correlated with alturaHigh correlation
altura is highly correlated with pesoHigh correlation
defense is highly correlated with s_defenseHigh correlation
s_defense is highly correlated with defenseHigh correlation
id is highly correlated with generacionHigh correlation
generacion is highly correlated with idHigh correlation
peso is highly correlated with alturaHigh correlation
altura is highly correlated with pesoHigh correlation
id is highly correlated with generacion and 1 other fieldsHigh correlation
generacion is highly correlated with id and 1 other fieldsHigh correlation
peso is highly correlated with altura and 3 other fieldsHigh correlation
altura is highly correlated with peso and 1 other fieldsHigh correlation
tipo1 is highly correlated with tipo2High correlation
tipo2 is highly correlated with id and 2 other fieldsHigh correlation
hp is highly correlated with peso and 3 other fieldsHigh correlation
attack is highly correlated with peso and 3 other fieldsHigh correlation
defense is highly correlated with peso and 3 other fieldsHigh correlation
s_attack is highly correlated with attackHigh correlation
s_defense is highly correlated with defenseHigh correlation
tipo2 has 456 (50.8%) missing values Missing
id is uniformly distributed Uniform
nombre is uniformly distributed Uniform
id has unique values Unique
nombre has unique values Unique

Reproduction

Analysis started2022-05-27 17:56:53.434393
Analysis finished2022-05-27 17:57:11.261406
Duration17.83 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct898
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449.5
Minimum1
Maximum898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-05-27T19:57:11.373423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.85
Q1225.25
median449.5
Q3673.75
95-th percentile853.15
Maximum898
Range897
Interquartile range (IQR)448.5

Descriptive statistics

Standard deviation259.3745683
Coefficient of variation (CV)0.5770290729
Kurtosis-1.2
Mean449.5
Median Absolute Deviation (MAD)224.5
Skewness0
Sum403651
Variance67275.16667
MonotonicityStrictly increasing
2022-05-27T19:57:11.493410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
6741
 
0.1%
5921
 
0.1%
5931
 
0.1%
5941
 
0.1%
5951
 
0.1%
5961
 
0.1%
5971
 
0.1%
5981
 
0.1%
5991
 
0.1%
Other values (888)888
98.9%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
8981
0.1%
8971
0.1%
8961
0.1%
8951
0.1%
8941
0.1%
8931
0.1%
8921
0.1%
8911
0.1%
8901
0.1%
8891
0.1%

nombre
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct898
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size56.9 KiB
bulbasaur
 
1
pancham
 
1
frillish
 
1
jellicent
 
1
alomomola
 
1
Other values (893)
893 

Length

Max length21
Median length19
Mean length7.751670379
Min length3

Characters and Unicode

Total characters6961
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique898 ?
Unique (%)100.0%

Sample

1st rowbulbasaur
2nd rowivysaur
3rd rowvenusaur
4th rowcharmander
5th rowcharmeleon

Common Values

ValueCountFrequency (%)
bulbasaur1
 
0.1%
pancham1
 
0.1%
frillish1
 
0.1%
jellicent1
 
0.1%
alomomola1
 
0.1%
joltik1
 
0.1%
galvantula1
 
0.1%
ferroseed1
 
0.1%
ferrothorn1
 
0.1%
klink1
 
0.1%
Other values (888)888
98.9%

Length

2022-05-27T19:57:11.597946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bulbasaur1
 
0.1%
octillery1
 
0.1%
mankey1
 
0.1%
kakuna1
 
0.1%
venusaur1
 
0.1%
charmander1
 
0.1%
charmeleon1
 
0.1%
charizard1
 
0.1%
squirtle1
 
0.1%
wartortle1
 
0.1%
Other values (888)888
98.9%

Most occurring characters

ValueCountFrequency (%)
a677
 
9.7%
e631
 
9.1%
o572
 
8.2%
r551
 
7.9%
i506
 
7.3%
l450
 
6.5%
n416
 
6.0%
t379
 
5.4%
s352
 
5.1%
u281
 
4.0%
Other values (20)2146
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter6913
99.3%
Dash Punctuation45
 
0.6%
Decimal Number3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a677
 
9.8%
e631
 
9.1%
o572
 
8.3%
r551
 
8.0%
i506
 
7.3%
l450
 
6.5%
n416
 
6.0%
t379
 
5.5%
s352
 
5.1%
u281
 
4.1%
Other values (16)2098
30.3%
Decimal Number
ValueCountFrequency (%)
51
33.3%
01
33.3%
21
33.3%
Dash Punctuation
ValueCountFrequency (%)
-45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6913
99.3%
Common48
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a677
 
9.8%
e631
 
9.1%
o572
 
8.3%
r551
 
8.0%
i506
 
7.3%
l450
 
6.5%
n416
 
6.0%
t379
 
5.5%
s352
 
5.1%
u281
 
4.1%
Other values (16)2098
30.3%
Common
ValueCountFrequency (%)
-45
93.8%
51
 
2.1%
01
 
2.1%
21
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6961
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a677
 
9.7%
e631
 
9.1%
o572
 
8.2%
r551
 
7.9%
i506
 
7.3%
l450
 
6.5%
n416
 
6.0%
t379
 
5.4%
s352
 
5.1%
u281
 
4.0%
Other values (20)2146
30.8%

generacion
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.146993318
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-05-27T19:57:11.813953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.248397641
Coefficient of variation (CV)0.5421753711
Kurtosis-1.097725726
Mean4.146993318
Median Absolute Deviation (MAD)2
Skewness0.1759072518
Sum3724
Variance5.055291953
MonotonicityIncreasing
2022-05-27T19:57:11.981959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5156
17.4%
1151
16.8%
3135
15.0%
4107
11.9%
2100
11.1%
889
9.9%
788
9.8%
672
8.0%
ValueCountFrequency (%)
1151
16.8%
2100
11.1%
3135
15.0%
4107
11.9%
5156
17.4%
672
8.0%
788
9.8%
889
9.9%
ValueCountFrequency (%)
889
9.9%
788
9.8%
672
8.0%
5156
17.4%
4107
11.9%
3135
15.0%
2100
11.1%
1151
16.8%

peso
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct439
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean639.7004454
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-05-27T19:57:12.213952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q185
median270
Q3650
95-th percentile2372.5
Maximum9999
Range9998
Interquartile range (IQR)565

Descriptive statistics

Standard deviation1194.302978
Coefficient of variation (CV)1.866972247
Kurtosis27.31190121
Mean639.7004454
Median Absolute Deviation (MAD)214.5
Skewness4.682708779
Sum574451
Variance1426359.604
MonotonicityNot monotonic
2022-05-27T19:57:12.550143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1011
 
1.2%
5010
 
1.1%
28010
 
1.1%
1209
 
1.0%
409
 
1.0%
859
 
1.0%
3009
 
1.0%
608
 
0.9%
38
 
0.9%
208
 
0.9%
Other values (429)807
89.9%
ValueCountFrequency (%)
15
0.6%
22
 
0.2%
38
0.9%
42
 
0.2%
55
0.6%
63
 
0.3%
71
 
0.1%
82
 
0.2%
91
 
0.1%
1011
1.2%
ValueCountFrequency (%)
99992
0.2%
95002
0.2%
92001
0.1%
88801
0.1%
82001
0.1%
80002
0.2%
75001
0.1%
68301
0.1%
65001
0.1%
55001
0.1%

altura
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.8596882
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-05-27T19:57:12.854143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median10
Q315
95-th percentile26.15
Maximum200
Range199
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.34221805
Coefficient of variation (CV)1.040686555
Kurtosis81.15072709
Mean11.8596882
Median Absolute Deviation (MAD)5
Skewness6.966495769
Sum10650
Variance152.3303464
MonotonicityNot monotonic
2022-05-27T19:57:12.982619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680
 
8.9%
470
 
7.8%
366
 
7.3%
1064
 
7.1%
564
 
7.1%
1253
 
5.9%
849
 
5.5%
1546
 
5.1%
743
 
4.8%
1139
 
4.3%
Other values (43)324
36.1%
ValueCountFrequency (%)
16
 
0.7%
223
 
2.6%
366
7.3%
470
7.8%
564
7.1%
680
8.9%
743
4.8%
849
5.5%
935
3.9%
1064
7.1%
ValueCountFrequency (%)
2001
0.1%
1451
0.1%
922
0.2%
881
0.1%
701
0.1%
651
0.1%
621
0.1%
581
0.1%
552
0.2%
541
0.1%

tipo1
Categorical

HIGH CORRELATION

Distinct18
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size54.7 KiB
water
123 
normal
109 
grass
86 
bug
75 
fire
58 
Other values (13)
447 

Length

Max length8
Median length7
Mean length5.265033408
Min length3

Characters and Unicode

Total characters4728
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgrass
2nd rowgrass
3rd rowgrass
4th rowfire
5th rowfire

Common Values

ValueCountFrequency (%)
water123
13.7%
normal109
12.1%
grass86
 
9.6%
bug75
 
8.4%
fire58
 
6.5%
psychic58
 
6.5%
rock50
 
5.6%
electric49
 
5.5%
dark36
 
4.0%
fighting36
 
4.0%
Other values (8)218
24.3%

Length

2022-05-27T19:57:13.238646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
water123
13.7%
normal109
12.1%
grass86
 
9.6%
bug75
 
8.4%
fire58
 
6.5%
psychic58
 
6.5%
rock50
 
5.6%
electric49
 
5.5%
fighting36
 
4.0%
dark36
 
4.0%
Other values (8)218
24.3%

Most occurring characters

ValueCountFrequency (%)
r598
12.6%
a406
 
8.6%
e367
 
7.8%
g337
 
7.1%
i328
 
6.9%
s326
 
6.9%
o326
 
6.9%
c292
 
6.2%
t269
 
5.7%
n253
 
5.4%
Other values (11)1226
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4728
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r598
12.6%
a406
 
8.6%
e367
 
7.8%
g337
 
7.1%
i328
 
6.9%
s326
 
6.9%
o326
 
6.9%
c292
 
6.2%
t269
 
5.7%
n253
 
5.4%
Other values (11)1226
25.9%

Most occurring scripts

ValueCountFrequency (%)
Latin4728
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r598
12.6%
a406
 
8.6%
e367
 
7.8%
g337
 
7.1%
i328
 
6.9%
s326
 
6.9%
o326
 
6.9%
c292
 
6.2%
t269
 
5.7%
n253
 
5.4%
Other values (11)1226
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII4728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r598
12.6%
a406
 
8.6%
e367
 
7.8%
g337
 
7.1%
i328
 
6.9%
s326
 
6.9%
o326
 
6.9%
c292
 
6.2%
t269
 
5.7%
n253
 
5.4%
Other values (11)1226
25.9%

tipo2
Categorical

HIGH CORRELATION
MISSING

Distinct18
Distinct (%)4.1%
Missing456
Missing (%)50.8%
Memory size41.4 KiB
flying
95 
poison
34 
fairy
33 
ground
32 
psychic
32 
Other values (13)
216 

Length

Max length8
Median length7
Mean length5.597285068
Min length3

Characters and Unicode

Total characters2474
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpoison
2nd rowpoison
3rd rowpoison
4th rowflying
5th rowflying

Common Values

ValueCountFrequency (%)
flying95
 
10.6%
poison34
 
3.8%
fairy33
 
3.7%
ground32
 
3.6%
psychic32
 
3.6%
dragon25
 
2.8%
fighting25
 
2.8%
steel24
 
2.7%
grass21
 
2.3%
ghost20
 
2.2%
Other values (8)101
 
11.2%
(Missing)456
50.8%

Length

2022-05-27T19:57:13.438632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flying95
21.5%
poison34
 
7.7%
fairy33
 
7.5%
ground32
 
7.2%
psychic32
 
7.2%
dragon25
 
5.7%
fighting25
 
5.7%
steel24
 
5.4%
grass21
 
4.8%
ghost20
 
4.5%
Other values (8)101
22.9%

Most occurring characters

ValueCountFrequency (%)
i278
11.2%
g252
 
10.2%
n217
 
8.8%
r190
 
7.7%
o166
 
6.7%
f166
 
6.7%
y160
 
6.5%
s152
 
6.1%
l133
 
5.4%
a122
 
4.9%
Other values (11)638
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2474
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i278
11.2%
g252
 
10.2%
n217
 
8.8%
r190
 
7.7%
o166
 
6.7%
f166
 
6.7%
y160
 
6.5%
s152
 
6.1%
l133
 
5.4%
a122
 
4.9%
Other values (11)638
25.8%

Most occurring scripts

ValueCountFrequency (%)
Latin2474
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i278
11.2%
g252
 
10.2%
n217
 
8.8%
r190
 
7.7%
o166
 
6.7%
f166
 
6.7%
y160
 
6.5%
s152
 
6.1%
l133
 
5.4%
a122
 
4.9%
Other values (11)638
25.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i278
11.2%
g252
 
10.2%
n217
 
8.8%
r190
 
7.7%
o166
 
6.7%
f166
 
6.7%
y160
 
6.5%
s152
 
6.1%
l133
 
5.4%
a122
 
4.9%
Other values (11)638
25.8%

hp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct102
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.0311804
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-05-27T19:57:13.574626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q150
median65
Q380
95-th percentile110
Maximum255
Range254
Interquartile range (IQR)30

Descriptive statistics

Standard deviation26.21370723
Coefficient of variation (CV)0.3797372011
Kurtosis7.477145754
Mean69.0311804
Median Absolute Deviation (MAD)15
Skewness1.688001125
Sum61990
Variance687.158447
MonotonicityNot monotonic
2022-05-27T19:57:13.798620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6074
 
8.2%
7064
 
7.1%
5061
 
6.8%
6550
 
5.6%
7546
 
5.1%
4046
 
5.1%
4544
 
4.9%
8043
 
4.8%
5538
 
4.2%
10035
 
3.9%
Other values (92)397
44.2%
ValueCountFrequency (%)
11
 
0.1%
101
 
0.1%
206
 
0.7%
254
 
0.4%
282
 
0.2%
3015
1.7%
311
 
0.1%
3516
1.8%
361
 
0.1%
371
 
0.1%
ValueCountFrequency (%)
2551
 
0.1%
2501
 
0.1%
2231
 
0.1%
2001
 
0.1%
1901
 
0.1%
1701
 
0.1%
1651
 
0.1%
1601
 
0.1%
1503
0.3%
1441
 
0.1%

attack
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct112
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.54454343
Minimum5
Maximum181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-05-27T19:57:14.110641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30
Q155
median75
Q395
95-th percentile130
Maximum181
Range176
Interquartile range (IQR)40

Descriptive statistics

Standard deviation29.66555905
Coefficient of variation (CV)0.3875594226
Kurtosis-0.345890652
Mean76.54454343
Median Absolute Deviation (MAD)20
Skewness0.2935451874
Sum68737
Variance880.0453938
MonotonicityNot monotonic
2022-05-27T19:57:14.326625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10042
 
4.7%
6041
 
4.6%
6541
 
4.6%
8041
 
4.6%
8538
 
4.2%
9037
 
4.1%
5037
 
4.1%
5536
 
4.0%
7536
 
4.0%
7034
 
3.8%
Other values (102)515
57.3%
ValueCountFrequency (%)
52
 
0.2%
103
 
0.3%
151
 
0.1%
2010
1.1%
221
 
0.1%
231
 
0.1%
241
 
0.1%
258
0.9%
271
 
0.1%
281
 
0.1%
ValueCountFrequency (%)
1811
 
0.1%
1651
 
0.1%
1602
0.2%
1504
0.4%
1471
 
0.1%
1451
 
0.1%
1431
 
0.1%
1404
0.4%
1391
 
0.1%
1372
0.2%

defense
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct107
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.88641425
Minimum5
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-05-27T19:57:14.438641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q150
median67
Q390
95-th percentile129.15
Maximum230
Range225
Interquartile range (IQR)40

Descriptive statistics

Standard deviation29.53618445
Coefficient of variation (CV)0.410872969
Kurtosis2.167346151
Mean71.88641425
Median Absolute Deviation (MAD)18
Skewness1.047886684
Sum64554
Variance872.386192
MonotonicityNot monotonic
2022-05-27T19:57:14.542627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5060
 
6.7%
6055
 
6.1%
7052
 
5.8%
8045
 
5.0%
4044
 
4.9%
6543
 
4.8%
9041
 
4.6%
5538
 
4.2%
4537
 
4.1%
10035
 
3.9%
Other values (97)448
49.9%
ValueCountFrequency (%)
52
 
0.2%
101
 
0.1%
154
 
0.4%
205
 
0.6%
231
 
0.1%
252
 
0.2%
282
 
0.2%
3017
1.9%
311
 
0.1%
322
 
0.2%
ValueCountFrequency (%)
2301
 
0.1%
2111
 
0.1%
2002
0.2%
1841
 
0.1%
1802
0.2%
1681
 
0.1%
1601
 
0.1%
1521
 
0.1%
1504
0.4%
1453
0.3%

speed
Real number (ℝ≥0)

Distinct117
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.94988864
Minimum5
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-05-27T19:57:14.654621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q145
median65
Q385
95-th percentile115
Maximum200
Range195
Interquartile range (IQR)40

Descriptive statistics

Standard deviation28.45659616
Coefficient of variation (CV)0.4314881609
Kurtosis0.09212427274
Mean65.94988864
Median Absolute Deviation (MAD)20
Skewness0.4437242026
Sum59223
Variance809.7778651
MonotonicityNot monotonic
2022-05-27T19:57:14.758625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5051
 
5.7%
6051
 
5.7%
6544
 
4.9%
7043
 
4.8%
3041
 
4.6%
4038
 
4.2%
4536
 
4.0%
8036
 
4.0%
8532
 
3.6%
9032
 
3.6%
Other values (107)494
55.0%
ValueCountFrequency (%)
53
 
0.3%
104
 
0.4%
131
 
0.1%
1512
1.3%
2016
1.8%
221
 
0.1%
234
 
0.4%
241
 
0.1%
2511
1.2%
261
 
0.1%
ValueCountFrequency (%)
2001
 
0.1%
1601
 
0.1%
1511
 
0.1%
1502
 
0.2%
1451
 
0.1%
1431
 
0.1%
1421
 
0.1%
1382
 
0.2%
1361
 
0.1%
1307
0.8%

s_attack
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct105
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.68151448
Minimum10
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-05-27T19:57:15.094641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q146.25
median65
Q390
95-th percentile125
Maximum173
Range163
Interquartile range (IQR)43.75

Descriptive statistics

Standard deviation29.37260938
Coefficient of variation (CV)0.4215265641
Kurtosis-0.1623685193
Mean69.68151448
Median Absolute Deviation (MAD)20
Skewness0.5730536741
Sum62574
Variance862.7501819
MonotonicityNot monotonic
2022-05-27T19:57:15.198620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4063
 
7.0%
6058
 
6.5%
5049
 
5.5%
6547
 
5.2%
5545
 
5.0%
4537
 
4.1%
8036
 
4.0%
7036
 
4.0%
10035
 
3.9%
9533
 
3.7%
Other values (95)459
51.1%
ValueCountFrequency (%)
103
 
0.3%
153
 
0.3%
2010
 
1.1%
231
 
0.1%
242
 
0.2%
2513
1.4%
272
 
0.2%
293
 
0.3%
3029
3.2%
311
 
0.1%
ValueCountFrequency (%)
1731
 
0.1%
1541
 
0.1%
1511
 
0.1%
1507
0.8%
1455
0.6%
1372
 
0.2%
1361
 
0.1%
1355
0.6%
1341
 
0.1%
1312
 
0.2%

s_defense
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.87639198
Minimum20
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-05-27T19:57:15.326645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile30.85
Q150
median65
Q385
95-th percentile120
Maximum230
Range210
Interquartile range (IQR)35

Descriptive statistics

Standard deviation27.01214227
Coefficient of variation (CV)0.3865703638
Kurtosis1.724817562
Mean69.87639198
Median Absolute Deviation (MAD)17
Skewness0.8834179979
Sum62749
Variance729.65583
MonotonicityNot monotonic
2022-05-27T19:57:15.446643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5064
 
7.1%
6053
 
5.9%
7050
 
5.6%
8049
 
5.5%
5548
 
5.3%
7545
 
5.0%
6545
 
5.0%
9041
 
4.6%
4540
 
4.5%
4039
 
4.3%
Other values (89)424
47.2%
ValueCountFrequency (%)
206
 
0.7%
231
 
0.1%
2512
1.3%
3026
2.9%
313
 
0.3%
321
 
0.1%
331
 
0.1%
341
 
0.1%
3523
2.6%
361
 
0.1%
ValueCountFrequency (%)
2301
 
0.1%
2001
 
0.1%
1543
0.3%
1505
0.6%
1421
 
0.1%
1403
0.3%
1381
 
0.1%
1353
0.3%
1321
 
0.1%
1312
 
0.2%

Interactions

2022-05-27T19:57:09.925240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:54.106973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:55.975238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:57.960383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:59.864941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:02.689323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:04.451956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:05.644811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:07.348828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:08.589019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:10.013222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:54.243002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:56.127259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:58.096383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:00.024967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:02.849310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:04.547959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:05.876940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:07.620843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:08.685015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:10.109230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:54.443290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:56.311238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:58.336358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:00.280947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:03.137383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:04.651952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:06.052813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:07.740831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:08.989015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:10.205232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:54.635297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:56.479245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:58.552667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:00.553043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:03.353422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:04.747959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:06.196813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:07.860817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:09.149022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:10.301229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:54.811292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:56.703654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:58.736661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:00.817318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:03.553408image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:04.875950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:06.316811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:07.973013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:09.317221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:10.389229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:54.987290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:56.879666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:58.904711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:01.081335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:03.665416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:04.963960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:06.508855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:08.069016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:09.421220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:10.487534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:55.139289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:57.063900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:59.112666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:01.361328image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:03.843973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:05.067953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:06.684812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:08.181016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:09.517231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:10.573072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:55.307299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:57.319904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:59.288682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:01.593325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:04.067959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:05.155958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:06.836832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:08.277016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:09.613230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:10.677410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:55.566937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:57.552359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:59.528943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:01.817337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:04.227968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:05.283951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:07.012825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:08.381022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:09.725221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:10.773390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:55.743261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:57.784359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:56:59.720938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:02.497311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:04.363958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:05.483963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:07.164831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:08.493012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:09.829240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-27T19:57:15.598623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:15.783161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:15.919147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:16.055178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T19:57:16.207569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2022-05-27T19:57:10.925399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-27T19:57:11.117395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-27T19:57:11.189395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idnombregeneracionpesoalturatipo1tipo2hpattackdefensespeeds_attacks_defense
01bulbasaur1697grasspoison454949456565
12ivysaur113010grasspoison606263608080
23venusaur1100020grasspoison80828380100100
34charmander1856fireNone395243656050
45charmeleon119011fireNone586458808065
56charizard190517fireflying78847810010985
67squirtle1905waterNone444865435064
78wartortle122510waterNone596380586580
89blastoise185516waterNone79831007885105
910caterpie1293bugNone453035452020

Last rows

idnombregeneracionpesoalturatipo1tipo2hpattackdefensespeeds_attacks_defense
888889zamazenta8210029fightingNone9213011513880115
889890eternatus89500200poisondragon140859513014595
890891kubfu81206fightingNone609060725350
891892urshifu-single-strike8105019fightingdark100130100976360
892893zarude870018darkgrass1051201051057095
893894regieleki8145012electricNone801005020010050
894895regidrago8200021dragonNone200100508010050
895896glastrier8800022iceNone1001451303065110
896897spectrier844520ghostNone100656013014580
897898calyrex87711psychicgrass1008080808080